Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels)

Lin Gu, Yinqiang Zheng, Ryoma Bise, Imari Sato, Nobuaki Imanishi, Sadakazu Aiso

研究成果: Conference contribution

20 被引用数 (Scopus)

抄録

In this paper, we focus on semi-supervised learning for biomedical image segmentation, so as to take advantage of huge unlabelled data. We observe that there usually exist some homogeneous connected areas of low confidence in biomedical images, which tend to confuse the classifier trained with limited labelled samples. To cope with this difficulty, we propose to construct forest oriented super pixels(voxels) to augment the standard random forest classifier, in which super pixels(voxels) are built upon the forest based code. Compared to the state-of-the-art, our proposed method shows superior segmentation performance on challenging 2D/3D biomedical images. The full implementation (based on Matlab) is available at https://github.com/lingucv/ssl_superpixels.

本文言語English
ホスト出版物のタイトルMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
編集者Maxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein
出版社Springer Verlag
ページ702-710
ページ数9
ISBN(印刷版)9783319661810
DOI
出版ステータスPublished - 2017
外部発表はい
イベント20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
継続期間: 2017 9 112017 9 13

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10433 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
国/地域Canada
CityQuebec City
Period17/9/1117/9/13

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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